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1. Taranpreet Kaur et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 3( Version 1), March 2014, pp.707-711
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Enhance the Driver Drowsiness Detection System Using
EDAMAS and Region Prop Techniques
Taranpreet Kaur, Sanjay Kumar Singh
ABSTRACT
Safe driving is a major concern of societies all over the world. Thousands of people are killed or seriously
injured due to drivers falling asleep at the wheels each year. Hence driver drowsiness is the major issue behind
accidents. To solve this issue many techniques are used. In our paper, we are going to use the region prop with
EDAMAS. In Driver drowsiness detection system region prop will helps to detect the size of eyes, whereas
EDAMAS is use to detect the swelling of soft tissues.
KEYWORDS: Driver Drowsiness, Region prop, Accident, EDAMAS.
I. INTRODUCTION:
Image Processing forms core research area
within engineering and computer science disciplines
too. Image processing basically includes the
following three steps.
 Importing the image with optical scanner or by
digital photography.
 Analyzing and manipulating the image which
includes data compression and image
enhancement and spotting patterns that are not to
human eyes like satellite photographs.
 Output is the last stage in which result can be
altered image or report that is based on image
analysis.
Driver Drowsiness System:
Driver drowsiness system is used to detect
the drowsiness. Drowsiness is the main reasons of
accident. Safe driving is a major concern of societies
all over the world. Thousands of people are killed or
seriously injured due to drivers falling asleep at the
wheels each year. It is essential to develop a real time
safety system for drowsiness related road accident
prevention. There are many methods for detecting the
driver drowsiness. Driver fatigue is a significant
factor in a large number of vehicle accidents.
It includes the measurements of
physiological features like EEG, heart rate, pulse
rate, eyelid movement, gaze, head movement and
behaviors of the vehicle, lane deviations and steering
movements. After long hours of driving or in absent
of alert mental state, the eyelids of driver will
become heavy due to fatigue. The attention of driver
starts to lose focus and that creates risks for
accidents. [1] These are typical reactions of fatigue,
which is very dangerous. Recent statistics estimate
that annually 1,200 deaths and 76,000 injuries can be
attributed to fatigue related crashes.
These accidents can be controlled by development of
technologies for detecting or preventing drowsiness.
The drowsiness detection fatigue is involves
sequence of images of a face. The analysis of face
images is a popular research area with applications
such as face recognition, virtual tools, and human
identification security systems.
The requirements for an effective drowsy driver
detection system are as follows:
 A non intrusive monitoring system that will
not distract the driver.
 A real time monitoring system, to insure
accuracy in detecting drowsiness.
 A system that will work in both daytime and
nighttime conditions.
Techniques for Detecting Drowsy Drivers
The techniques for driver drowsiness can be
classified into different categories. [2] As,
 Sensing of physiological characteristics
 Sensing of driver operation
 Sensing of vehicle response
 Monitoring the response of driver.
Now days, there are many road accidents
occur. Driver drowsiness detection is a car
safety technology which prevents accidents when the
driver is getting drowsy. There are many reasons of
the road accidents. The reasons of the road accidents
are
RESEARCH ARTICLE OPEN ACCESS

2. Taranpreet Kaur et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 3( Version 1), March 2014, pp.707-711
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Figure 1: Reasons of drowsiness [3]
There are many functions that can use in the
driver drowsiness.
 The driver drowsiness can be detect by analyzing
the driving behavior
 Warns the driver if the risk is high of falling
asleep
Sleepiness and driving is a dangerous
combination. Most people are aware of the dangers of
drinking and driving. The people don’t realize that
drowsy driving can be just as fatal, for example:
alcohol, sleepiness slows reaction time, decreases
awareness, impairs judgment and increases your risk
of crashing. There are many underlying causes of
sleepiness, fatigue and drowsy driving. It includes the
sleep loss from the restriction, interruption or
fragmented sleep; chronic sleep debt; circadian
factors associated with driving patterns or work
schedules; undiagnosed or untreated sleep disorders;
time spent on a task; the use of sedating medications;
and the consumption of alcohol when already tired.
These factors have cumulative effects and a
combination of any of these can greatly increase
one’s risk for a fatigue-related crash.
Signs Of Drowsy Driving
There are many signs of the driver’s
drowsiness:
 Driver may be yawn frequently.
 Driver is unable to keep eyes open.
 Driver catches him nodding off and has trouble
keeping head up.
 The thoughts of the person wander and take
focus off from the road.
 The driver can't remember driving the last few
miles.
 Driver is impatient, in a hurry, and grouchy.
 The person ends up too close to cars in front of
you.
 The person misses road signs or drive past your
turn.
 Drift into the other lane or onto the shoulder of
the road.
II. LITERATURE SURVEY
A Dedicated System for Monitoring of Driver’s
Fatigue K.Subhashini Spurjeon, Yogesh
Bahindwar, (2012): describe about the road
accidents. The road accidents happen due to the lack
of attention of the driver. In this paper author
describes a real time system for analyzing video
sequences of a driver and determining the level of
attention. For this purpose, author uses the
computation of percent of eyelid closure. The eye
closure acts as an indicator to detect drowsiness.
Driver’s fatigue and drowsiness are the major causes
of traffic accidents on road. It is very necessary to
monitor the driver’s vigilance level and to issuing an
alert when he/she is not paying enough attention to
the road is a promising way to reduce the accidents
caused by driver factors. The fatigue monitoring can
be starts with extracting visual parameters. This can
be done via a computer vision system. In the
purposed work, author purpose a real time robust
methods for eye tracking under variable lighting
conditions and facial orientations. In this paper the
latest technologies in pattern classification
recognition and in object tracking are employed for
eye detection. [4] The tracking is based on the eye
appearance. Visual information is acquired using a
specially designed solution combining a CCD video
camera with an IR illumination system. The system is
fully automatic and detects eye position and eye
closure and recovers the gaze of eyes. Experimental
results using real images demonstrate the accuracy
and robustness of the proposed solution. This could
become an important part in the development of the
advanced safety vehicle.
Drowsiness Warning System Using Artificial
Intelligence, Nidhi Sharma, V. K. Banga, [2010]:
in this paper author discuss about the various
artificial intelligence methods for detecting the
drowsiness of system. Driver’s drowsiness is an
important factor in motoring of vehicle from
accidents. The driving performance deteriorates with
increased drowsiness with resulting crashes
constituting morel vehicle accidents. In recent years,
there has been growing interest in intelligent
vehicles. [5] The ongoing intelligent vehicle research
will revolutionize the way vehicles and drivers
interact in the future. The detection mechanism into
vehicles may help prevent many accidents. There are

3. Taranpreet Kaur et al Int. Journal of Engineering Research and Applications www.ijera.com
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various techniques used for analyzing driver
exhaustion. Most of the published research on
computer vision approaches to detection of fatigue
has focused on the analysis of blinks and head
movements. After long hours of driving or in absence
of mental alert state, the attention of driver starts to
loose and that creates risks of accidents. These are
the typical reactions of fatigue, which are very
dangerous. In image fatigue detection, correct and
real time decision is very important. In this paper,
author discusses the various artificial detection.
A Yawning Measurement Method to Detect
Driver Drowsiness, Behnoosh Hariri, et.al:
Describe that the drowsy is the major issue
behind the road accidents. The use of assistive
systems that monitor a driver’s level of vigilance and
alert the driver in case of drowsiness can be
significant in the prevention of accidents. In this
paper author purposed a new approach towards
detection of drives drowsiness based on yawning
measurement. [6] This involves several steps
including the real time detection and tracking of
driver’s face, detection and tracking of the mouth
contour and the detection of yawning based on
measuring both the rate and the amount of changes in
the mouth contour area. In this paper several
techniques are used, that are applied several
techniques to ensure the robust detection of yawning
expression in the presence of variable lighting
conditions and facial occlusions. Test results
demonstrate that the proposed system can efficiently
measure the aforementioned parameters and detect
the yawning state as a sign of driver’s drowsiness.
DEVELOPMENT OF A DROWSINESS
WARNING SYSTEM USING NEURAL
NETWORK, Itenderpal singh1, Prof. V.K.Banga,
(2013): describe the facial image analysis. As due to
the increase in the amount of automobile the
problems created by accidents have become more
complex. The transportation system is no longer
sufficient. Hence the research upon the safety of the
vehicles is the recent topic nowadays. In this paper
author discuss about the safety warning systems. This
system is active warning systems for preventing
traffic accidents have been attracting much public
attention. Safe driving is a major concern of today’s
societies. There are thousands of accidents are
happen in a day. [7] Due to which many people get
injured and many out of them got die. The aim of this
paper is to develop a prototype drowsiness detection
system. The main focus is on designing a system that
are used for l accurately monitor the open or closed
state of the driver’s eyes in real time. By monitoring
the eyes, it is believed that the symptoms of driver
fatigue can be detected early enough to avoid a car
accident. The author purposed a vehicle driver
drowsiness warning system using image processing
technique with neural network. it is based on facial
images analysis for warning the driver of drowsiness
or inattention to prevent traffic accidents. The facial
images of driver are taken by the video camera that is
installed on the dashboard in front of the driver. A
Neural network based algorithm is proposed to
determine the level of fatigue. It measures by the eye
opening and closing, and warns the driver
accordingly.
III. PURPOSED WORK
The various methods available to determine
the drowsiness state of a driver. But because of the
various definitions and the reasons behind them were
discussed there is no universally accepted definition
for drowsiness, all works discuses about the various
ways in which drowsiness can be manipulated in a
simulated environment. The various measures used to
detect drowsiness include the subjective, vehicle-
based, physiological and behavioral measures. The
accuracy rate of using physiological measures to
detect the drowsiness is high. These measures are
highly intrusive. The intrusive nature can be resolved
by using contactless electrode placement.
Figure 2: drowiness of driver
The development of technologies for
detecting or preventing drowsiness at the wheel is a

4. Taranpreet Kaur et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 3( Version 1), March 2014, pp.707-711
www.ijera.com 710 | P a g e
major challenge in the field of accident avoidance
systems. Because of the hazard that drowsiness
presents on the road, methods need to be developed
for counteracting its affects. To overcome these
researchers developed lots of techniques like a
prototype drowsiness detection system. The focus
was placed on designing of system that was
accurately monitoring the open or closed state of the
driver’s eyes in real-time. The symptoms of driver
fatigue can be detected by monitoring the eye
movement. It helps to avoid the accidents. Detection
of fatigue involves the sequences of images of a face.
It also observes the eye movements and blink
patterns.
3.2 Objectives:
For the proposed work our objectives are:
 Introduce the novel approach for preventing
driver drowsiness.
 Accurately measure are the symptoms of
drowsiness like:
 Case when the driver’s head is tilted.
 Finding the top of the head correctly.
 Finding bounds of the functions.
 Implement the novel approach in simulation
environment.
3.3 Methodology:
Driver drowsiness detection system: This is used
for car safety. It is a technology which prevents
accidents when the driver is getting drowsy during
driving. This system detects driver’s eyes, face or
body moment and gives alarm to wake driver. Now
days some of the Driver drowsiness detection
systems learn driver patterns and can detects when a
driver is becoming drowsy means basically this
system is used for safety of driver.
Here to detect Driver drowsiness we are going to use
a new scenario. In this proposed scenario we are
going to use “region props” and “EDAMAS”
techniques.
Region props: it is a detection technique. In Driver
drowsiness detection system It will detect the size of
eyes. As we know that during drowsiness size of our
eyes gets small. So region props will check the size
and if it will became small then it detects drowsiness
and will gives alarm to user.
Normal size of eyes
Eyes during drowsiness
EDAMAS: it’s a technique use to detect the swelling
of soft tissues. As we know that during drowsiness
our eyes gets swelled. So here we are going to use
EDAMAS technique so that it can detects that
drowsiness swelling of eyes of driver and can gives
alarm.
Here we will merge both techniques and develop a
new Driver drowsiness detection system.
Flow Chart:
Figure 3: Driver drowsiness

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ISSN : 2248-9622, Vol. 4, Issue 3( Version 1), March 2014, pp.707-711
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REFERENCES
[1] http://umpir.ump.edu.my/1978/1/Ani_Syaza
na_Jasni_(_CD_5355_).pdf
[2] http://www.ee.ryerson.ca/~phiscock/thesis/d
rowsy-detector/drowsy-detector.pdf
[3] http://www.sae.org/events/gim/presentations
/2012/sgambati.pdf
[4] K.Subhashini Spurjeon 1, Yogesh
Bahindwar, A Dedicated System for
Monitoring of Driver’s Fatigue,
International Journal of Innovative Research
in Science, Engineering and Technology
Vol. 1, Issue 2, December 2012 Copyright to
IJIRSET www.ijirset.com 256
[5] Nidhi Sharma, V. K. Banga, Drowsiness
Warning System Using Artificial
Intelligence, World Academy of Science,
Engineering and Technology 43 2010
[6] Behnoosh Hariri, Shabnam Abtahi, Shervin
Shirmohammadi, Luc Martel, A Yawning
Measurement Method to Detect Driver
Drowsiness.
[7] Itenderpal singh, Prof. V.K.Banga
,DEVELOPMENT OF A DROWSINESS
WARNING SYSTEM USING NEURAL
NETWORK, International Journal of
Advanced Research in Electrical,
Electronics and Instrumentation Engineering
(An ISO 3297: 2007 Certified Organization)
Vol. 2, Issue 8, August 2013